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Extending the Soar Cognitive Architecture

Explore groundbreaking advancements in cognitive architecture through extending the Soar model. Learn about symbolic and episodic memories, reinforcement learning, non-symbolic reasoning, and more. Discover the potential to integrate with perception and motor systems, mastering world knowledge and language skills.

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Extending the Soar Cognitive Architecture

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  1. Extending the Soar Cognitive Architecture John E. Laird University of Michigan AGI Conference March 1, 2008 http://sitemaker.umich.edu/soar/home

  2. Extending Soar Symbolic Long-Term Memories • Learn from internal rewards • Reinforcement learning • Learn facts • What you know • Semantic memory • Learn events and situtations • What you remember • Episodic memory • Basic drives and … • Emotions, feelings, mood • Non-symbolic reasoning • Mental imagery • Learn from regularities • Spatial and temporal clusters Procedural Episodic Episodic Semantic Semantic Chunking Semantic Learning Semantic Learning Episodic Learning Episodic Learning Reinforcement Learning Reinforcement Learning Symbolic Short-Term Memory Decision Procedure Appraisal Detector Appraisal Detector Perception Action Visual Imagery Visual Imagery Clustering Clustering Body

  3. Select Operator Apply Operator Elaborate State Elaborate Operator Input Apply Output Propose Operators Evaluate Operators Soar Processing Cycle Decide Manipulate Visual Imagery Create Motor Commands Query Semantic, Episodic Memory, Visual Imagery Perception Rules Impasse Subgoal

  4. Theoretical Commitments Stayed the Same Changed Multiple long-term memories Multiple learning mechanisms Modality-specific representations & processing Non-symbolic processing Symbol generation (clustering) Control (numeric preferences) Learning Control (reinforcement learning) Intrinsic reward (appraisals) Aid memory retrieval (WM activation) Non-symbolic reasoning (visual imagery) • Problem Space Computational Model • Long-term & short-term memories • Associative procedural knowledge • Fixed decision procedure • Impasse-driven reasoning • Incremental, experience-driven learning • No task-specific modules

  5. Upcoming Challenges • Continued refinement and integration • Integrate with complex perception and motor systems • Adding/learning lots of world knowledge • Language, Spatial, Temporal Reasoning, … • Scaling up to large bodies of knowledge

  6. Thanks to Funding Agencies: NSF, DARPA, ONR Ph.D. students: Nate Derbinsky, Nicholas Gorski, Scott Lathrop, Robert Marinier, Andrew Nuxoll, Yongjia Wang, Samuel Wintermute, Joseph Xu Research Programmers: Karen Coulter, Jonathan Voigt Continued inspiration: Allen Newell

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